Integrating React.js and Shiny, and UCI Capstone

By John Peach | June 25, 2019

This month we have Alan Dipert from RStudio coming to talk about integrating React.js and Shiny. Alan is one of the key developers behind the development of Shiny and we are so lucky to have him there to share his knowledge.

The Paul Merage School of Business at UCI is graduating their second cohort of students from their MS in Business Analytics program. Last year we invited them to present their Capstone projects and it was a huge success, so they are back this year. We will have two groups presenting. One worked with the Amazon Alexa Skill Store and one worked with Pacific Life. Check out their abstracts for more details.

Come early and network. Do not forget to purchase a raffle ticket or two. It helps support the meet-up and we have a great prize. A book of your choice from CRC Press. Yup, you get to chose the book!

== Schedule ==

6:30 - 6:50 Networking

6:50 - 7:00 Welcome & general announcement

7:00 - 7:15 UCI - Pacific Life

7:15 - 7:30 UCI - Amazon Alexa Skill Store

7:30 - 8:00 Integrating React.js and Shiny

8:00 - Raffle

8:00 - 8:30 Networking and Clean-up

== UCI MSBA Capstone Talks ==

Pacific Life: Predicting policyholder lapse behavior

Applying the principles of Statistics and Machine Learning to predict policyholder lapse rate for Variable Annuities. In addition, we gained insight into the underlying dynamics of policyholder lapse behavior.

Amazon Alexa Skill Store:

Using effectively the skill intent model (a description of a third party application), including but not limited to, description, sample utterances, keywords, maturity rating, etc. we try to classify Alexa skills into one of 20 categories. Creating features with text data has a two-fold approach. The first is to use frequency of words to give words to each word occurring in the skill-intent model and use this matrix to predict the category as a supervised classification problem. The second is to use the semantic meaning, that is similarity of words, defined by cosine similarity. To overcome the big challenge of overlapping categories, we use a probabilistic approach to categorization.

== Integrating React.js and Shiny ==

Speaker: Alan Dipert

React.js is a thriving JavaScript library that eases encapsulating and sharing sophisticated component libraries. The React.js ecosystem is filled with components for doing everything from color selection (react-color) to animation (react-spring). While it’s always been technically possible to integrate React.js components with Shiny applications, it hasn’t always been particularly obvious how. To make it easier, we augmented the excellent reactR package with functions specifically designed to make it easier to create new htmlwidgets, inputs, and outputs based on React.js components. In this talk, I will further motivate and demonstrate these new tools and do my best to empower the audience to try them out.

comments powered by Disqus